An information theory based approach for identifying influential spreaders in temporal networks

Abstract

Identifying the most influential nodes in computer networks is an important issue in preventing the spread of computer viruses. In order to quantify the importance of nodes in the spreading of computer viruses, various centrality measures have been developed under an assumption of a static network. These measures have limitations in that many network structures are dynamically change over time. In this paper, we extend an entropy-based centrality from time-independent networks to time-dependent networks by taking into account the temporal and spatial connections between different nodes simultaneously. We also propose an algorithm for ranking the influences of nodes. According to the experimental results on three synthetic networks and a real network for susceptible-infected-recovered (SIR) spreading model, our proposed temporal entropy-based centrality (TEC) is more accurate than existing temporal betweenness, and closeness centralities.